Download Balanced Codon Usage Optimizes Eukaryotic Translational Efficiency

Balanced Codon Usage Optimizes Eukaryotic
Translational Efficiency
Wenfeng Qian1, Jian-Rong Yang1,2, Nathaniel M. Pearson1¤, Calum Maclean1, Jianzhi Zhang1*
1 Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America, 2 Key Laboratory of Gene Engineering of the
Ministry of Education, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
Abstract
Cellular efficiency in protein translation is an important fitness determinant in rapidly growing organisms. It is widely
believed that synonymous codons are translated with unequal speeds and that translational efficiency is maximized by the
exclusive use of rapidly translated codons. Here we estimate the in vivo translational speeds of all sense codons from the
budding yeast Saccharomyces cerevisiae. Surprisingly, preferentially used codons are not translated faster than unpreferred
ones. We hypothesize that this phenomenon is a result of codon usage in proportion to cognate tRNA concentrations, the
optimal strategy in enhancing translational efficiency under tRNA shortage. Our predicted codon–tRNA balance is indeed
observed from all model eukaryotes examined, and its impact on translational efficiency is further validated experimentally.
Our study reveals a previously unsuspected mechanism by which unequal codon usage increases translational efficiency,
demonstrates widespread natural selection for translational efficiency, and offers new strategies to improve synthetic
biology.
Citation: Qian W, Yang J-R, Pearson NM, Maclean C, Zhang J (2012) Balanced Codon Usage Optimizes Eukaryotic Translational Efficiency. PLoS Genet 8(3):
e1002603. doi:10.1371/journal.pgen.1002603
Editor: Harmit S. Malik, Fred Hutchinson Cancer Research Center, United States of America
Received September 29, 2011; Accepted February 5, 2012; Published March 29, 2012
Copyright: ß 2012 Qian et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by research grants from the U.S. National Institutes of Health to JZ. J-RY was supported in part by the 985 Project Fund from
Sun Yat-sen University. This article was made available as Open Access with the support of the University of Michigan COPE Fund. The funders had no role in
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: [email protected]
¤ Current address: Knome, Cambridge, Massachusetts, United States of America
actively engaged in translation during rapid cell growth [13–14]
and that ribosome concentration increases with the rate of cell
growth [15]. An important observation invoked to support the
efficiency hypothesis is that cognate tRNAs of preferred codons
tend to have higher cellular concentrations (or more gene copies)
than those of unpreferred codons [4,16], which may allow faster
translation of preferred codons than unpreferred codons. While
results from several earlier studies are consistent with this
hypothesis [12,17], these studies do not exclude the possibility
that the observed differences in activity or fitness caused by
synonymous mutations are entirely due to CUB’s influence on
translational accuracy (see Discussion). Here we directly test the
efficiency hypothesis and its presumed underlying mechanism.
Introduction
Eighteen of the 20 amino acids are each encoded by two or
more synonymous codons in the standard genetic code, yet the
synonymous codons are often used unequally in a genome. Such
codon usage bias (CUB) has been extensively documented in all
three domains of life [1–3]. Within a genome, highly expressed
genes tend to have stronger CUB than lowly expressed ones [4],
and the codons preferentially used in highly expressed genes of a
species are referred to as preferred codons.
Although codon usage is clearly determined by the joint actions
of mutation, drift, and selection [5–6], the fitness benefit of CUB is
less clear. There are two prevailing, non-mutually exclusive,
hypotheses on the selective utility of CUB: accuracy and efficiency
of protein translation [6]. The translational accuracy hypothesis
asserts that different synonymous codons have different probabilities of mistranslation, and that the use of accurately translated
codons is beneficial because mistranslation reduces the number of
functional molecules, wastes energy, and/or induces cytotoxic
protein misfolding. Unequivocal evidence for this hypothesis exists
[7–10].
By contrast, the translational efficiency hypothesis lacks direct
evidence. This hypothesis holds that different synonymous codons
are translated at different speeds, and that faster translation is
beneficial because it minimizes ribosome sequestering and so helps
alleviate ribosome shortage [5,11–12]. The relevance of ribosome
shortage is evident from the findings that most ribosomes are
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Results
Estimating in vivo translational speeds
The translational efficiency hypothesis assumes that synonymous codons have different translational speeds, caused by
disparities in codon selection time (CST), the time needed for
ribosomal A site to find the cognate ternary complex of
aminoacylated tRNA+eEF-1a+GTP. To test this proposition, we
took advantage of a genome-wide ribosome profiling study of
Saccharomyces cerevisiae that surveyed ribosome-protected mRNA
fragments at a nucleotide resolution in a cell population at a given
moment by Illumina deep sequencing [18]. Because the
probability that a codon is docked at the A site is proportional
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Codon Usage and Translational Efficiency
for multiple testing by the Bonferroni correction, only two amino
acids showed significant differences. The highest-RSCU’ codon has
a lower CST than the lowest-RSCU’ codon for glycine (nominal
P = 0.002), while the opposite is true for arginine (nominal
P,0.001). Our results are robust to different multiple-testing
corrections, as no other amino acids show a nominal P,0.01.
Furthermore, when RSCU’ is not considered, arginine is the only
amino acid for which synonymous codons show significant
heterogeneity in CST at the 5% significance level after the
correction for multiple testing. Following an earlier study [1], we
also tried defining preferred codons without using gene expression
data, but the results are not different (Figure S4). The overall lack
of a significant negative correlation between CST and synonymous
codon usage is real rather than an artifact of imprecise CST
estimation, because the standard errors of CSTs are quite small
(Figure 1A) and CSTs of several nonsynonymous codons differ
significantly from one another (see below).
To validate the above findings, we also directly compared
RSCU’ values of individual codon positions of Illumina reads from
the ribosome profiling data, without estimating CSTs. If unpreferred codons are translated more slowly and therefore stay at the
ribosomal A site longer than preferred codons, codons at the A site
should have a lower RSCU’ on average than its neighboring sites of
the same read, after the correction of sequencing bias by mRNASeq data. However, we observed no dip in RSCU’ at the A site
(Figure 1E). We further calculated, within each gene, the ratio
between the frequency of preferred codons and that of unpreferred
codons at the ribosome A site of Illumina reads from the ribosome
profiling data, after correction by mRNA-Seq. This ratio is
expected to be 1 if preferred and unpreferred codons are
translated equally fast. Indeed, after combining the ratio for all
amino acids and all genes using the Mantel-Haenszel procedure
[23], we found the overall ratio to be 0.984, not significantly
different from 1 (P = 0.21, two-tail x2 test).
Author Summary
Although an amino acid can be encoded by multiple
synonymous codons, these codons are not used equally
frequently in a genome. Biased codon usage is believed to
improve translational efficiency because it is thought that
preferentially used codons are translated faster than
unpreferred ones. Surprisingly, we find similar translational
speeds among synonymous codons. We show that
translational efficiency is optimized by a previously
unknown mechanism that relies on proportional use of
codons according to their cognate tRNA concentrations.
Our results provide important molecular details of protein
translation, answer why codon usage is unequal, demonstrate widespread natural selection for translational
efficiency, and can guide designs of synthetic genomes
and cells with efficient translation systems.
to its CST, we estimated the relative CSTs of all 61 sense codons
(Figure 1A) by the ratio of the observed codon frequencies at the A
site in the ribosome profiling data and the expected codon
frequencies estimated from mRNA-Seq data generated under the
same condition in the same experiment (Figures S1, S2, S3; see
Materials and Methods). The standard errors of the CST estimates,
measured by bootstrapping genes from the original datasets, are
on average 12% of the CST estimates (Figure 1A), indicating that
our CST estimates are overall quite precise.
CUB is commonly measured by the relative synonymous codon usage
(RSCU), defined by the frequency of a codon relative to the
average frequency of all of its synonymous codons in a set of highly
expressed genes [19]. To compare the usage of all 61 sense codons,
we also use RSCU’, which is the proportion of use of a given codon
among synonymous choices in a set of highly expressed genes (see
Materials and Methods). Another commonly used measure of
CUB is the codon adaptation index (CAI) [20], which is calculated for a
gene, and measures its usage of high-RSCU codons (see Materials
and Methods). The greater the CAI, the more prevalent are
preferred codons in the gene.
Contrary to the widely held presumption that preferred codons
are translated faster than unpreferred codons, no significant
negative correlation between RSCU’ and CST was observed among
the 61 sense codons (Figure 1B). It is also believed that codons with
abundant cognate tRNAs tend to have low CSTs. Because tRNA
gene copy number and tRNA concentration are highly positively
correlated [21–22], the former is often used as a proxy of the
latter. However, neither tRNA gene copy number (Figure 1C) nor
tRNA concentration (Figure 1D) correlates negatively with CST.
Because codons and tRNAs do not have one-to-one correspondence, in the foregoing analysis, we considered the best-matching
tRNA species for each codon. This codon-tRNA relationship has
been shown to be more accurate than the wobble rule, at least in
yeast [22].
We also examined each amino acid separately. Among the 18
amino acids with at least two codons, 12 (Ala, Asn, Cys, Gln, Glu,
Gly, Ile, Lys, Ser, Thr, Tyr, and Val) showed a negative
correlation between RSCU’ and CST, while 6 (Arg, Asp, His,
Leu Phe, and Pro) showed a positive correlation, when statistical
significance of the correlation was not required (Figure 1A). The
number of negative correlations is not significantly more than the
chance expectation of 9 (P = 0.12, one-tail sign test).
Using the standard errors of the CST estimates for the foregoing
18 amino acids (Figure 1A), we tested whether the CSTs are
significantly different between the synonymous codon with the
highest RSCU’ and that with the lowest RSCU’. After the control
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Optimal codon usage under tRNA shortage
The above findings are puzzling, because the first step in the
interaction between tRNA and mRNA is non-specific [24] and the
relative waiting time for the cognate tRNA to arrive at the
ribosome A site is expected to be inversely proportional to the
relative concentration of the cognate tRNA. It was also reported
that CST is the rate-limiting step in translational elongation [25].
The only plausible explanation of similar CSTs among synonymous codons is that, in wild-type yeast cells for which the ribosome
profiling was conducted, available cognate tRNAs for translating
synonymous codons have effectively the same concentration.
In rapidly growing yeast, ,80% of total RNA is rRNA and
,15% is tRNA [15]. The mean length of yeast tRNAs is ,72
nucleotides and the total length of rRNAs per ribosome is 5469
nucleotides [15]. Thus, the number of tRNA molecules per cell is
approximately (15%/72)/(80%/5469) = 14.2 times the number of
ribosomes per cell, substantially exceeding the expected ratio of
two tRNAs per active ribosome (at A and P sites, respectively) if
tRNA recharging and diffusion is instantaneous.
In reality, however, tRNA recycling takes time and thus cannot
be ignored. Each tRNA, after completing its job of transferring an
amino acid to the elongating peptide and then exiting the
ribosomal E site, needs to be recharged with the cognate amino
acid and then with eEF-1a+GTP to form a ternary complex
before it can be reused in translation. It has been estimated that
each ribosome translates ,32.6 codons per second in yeast [26].
This implies that on average a tRNA molecule needs to be used
32.6/14.2 = 2.3 times per second, or once every 0.44 second. It is
possible that the time for ternary complexes to form and diffuse to
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Figure 1. Relative codon selection times (CSTs) in wild-type yeast cells in rich media. (A) CST (grey bars) and RSCU’ (orange dots) of each
sense codon. CSTs are rescaled such that the maximal observed value is 1. Error bars show one standard error, estimated by the bootstrap method.
No significant negative correlation between CST and (B) RSCU’, (C) tRNA gene copy number, or (D) tRNA concentration. Spearman’s rank correlation
coefficients (r) and associated P values are presented above each panel. The P value in (B) is calculated by a permutation test because of the nonindependence among RSCU’ values of synonymous codons. (E) No dip in RSCU’ at the ribosomal A site, compared to P, E, and other neighboring sites.
Geometric means of RSCU’ is calculated at each codon position (as in the calculation of CAI) for ribosome profiling sequencing reads and mRNA
sequencing reads, respectively; the ratio at each position is presented. Error bars show one standard error estimated by bootstrapping sequencing
reads 1000 times.
doi:10.1371/journal.pgen.1002603.g001
ternary complex is formed again [27]. This observation and its
explanation strongly implies that the local concentration of ternary
complexes is low; otherwise, the addition of one cognate tRNA
molecule among on average 20 tRNAs (because identical amino
acids are expected to be on average 20 residues apart) cannot
significantly increase the relative concentration of the cognate
tRNA around the ribosome. Based on available information in E.
ribosomal A site is a substantial fraction of 0.44 second, so that the
local concentration of ternary complexes is much lower than the
total tRNA concentration. A recent study reported that consecutive synonymous codons in an mRNA tend to use the same tRNA
and proposed that this codon choice is beneficial because a tRNA
does not diffuse far from the ribosome after exiting its E site and is
reused for translating the next synonymous codon when the
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support the following model that includes three elements: (1)
available tRNAs are in shortage during translation, (2) translational efficiency is optimized in nature by balanced codon usage
according to tRNA concentrations, and (3) synonymous codons
are translated with similar speeds under the codon-tRNA
balance. Our model predicts reduced translational efficiency
due to ribosome sequestering when the codon-tRNA balance is
broken. It further predicts lower efficiency under exclusive use of
preferred codons than balanced use of preferred and unpreferred
codons.
We experimentally tested the above predictions by quantifying
the cellular efficiency in translation, represented by the protein
expression of a reporter gene, under different levels of codontRNA imbalance induced by the expression of another gene.
Unlike previous studies [12,17], our separation of the inducer and
reporter allows the distinction among several potential mechanisms of CUB’s impact on protein expression. We inserted our
reporter gene, the Venus yellow fluorescent protein (vYFP) gene
controlled by the GPD promoter, into Chromosome XII of a
haploid strain of S. cerevisiae (Figure 3A). We then designed four
synonymous sequences encoding another fluorescent protein,
mCherry, as our inducer (Figure S5). The four mCherry sequences,
named mCherry-1, 2, 3, and 4, cover the entire range of CAI of
native yeast genes (Figure 3B). We developed an index, distance to
native codon usage (Dncu), to measure the difference between the
codon usage of a (heterologous) gene and the overall codon usage
of the host cell, which is proportional to tRNA concentrations (see
Materials and Methods). The four mCherry versions also span a
large range of Dncu (Figure 3C) and show different degrees of
codon-tRNA imbalance for individual amino acids (Figure S6).
Other than synonymous codon usage, the four mCherry versions are
nearly identical: they encode the same protein sequence, have
similar G+C content (42–44%), and have identical sequences in
the first 56 nucleotides of the coding region, because this region
may affect the level of protein expression [12,32–33]. Each mCherry
gene is expressed from a constitutive and strong promoter on a
high-copy-number plasmid (see Materials and Methods). The four
plasmids were separately transformed to yeast cells carrying the
vYFP reporter gene (Figure 3A). Our model predicts that the
higher the Dncu of mCherry, the lower the vYFP expression.
The four yeast strains were grown in rich media to the log
phase, and the expression levels of vYFP and mCherry proteins
were inferred from their fluorescent signals, which were simultaneously measured for each cell by fluorescence-activated cell
scanning of at least 300,000 cells. We found mCherry expression
levels to be significantly different among the four strains (see
Materials and Methods). Within each strain, expression levels of
mCherry and vYFP are negatively correlated among cells (see
Materials and Methods). Hence, the expressions of vYFP cannot
be directly compared among strains. Instead, we separated the
cells of each strain into three bins on the basis of mCherry
expression and then compared vYFP expressions among the four
strains for cells with similar mCherry expressions (Figure 3D). We
found that, across the range of mCherry expressions shared by the
four strains, the higher the Dncu of mCherry, the lower the
expression of vYFP (Figure 3D). Furthermore, the vYFP
expression-level difference among the strains increases with the
mCherry expression level (Figure 3D). Of special interest is the
comparison between mCherry-3 and mCherry-4, which clearly shows
that it is a low Dncu rather than a high CAI that enhances
translational efficiency (Figure 3D). A multivariate regression
analysis of all cells from the four strains further demonstrated that
Dncu is significantly more important than CAI in explaining the
variation of the vYFP signal (P,0.001).
coli, we calculated that the physiological concentration of ternary
complexes is only ,4.3% of the total concentration of tRNAs and
,22% of the concentration of ribosomes (see Materials and
Methods). These observations strongly support our hypothesis that
available tRNA is in shortage during translation. Consistent with
our hypothesis, total tRNA concentrations increase with the rate of
cell growth in E. coli [28] and tRNA gene copy number increases
with the shortening of the minimal generation time across species
[29].
Under tRNA shortage, the optimal usage of synonymous
codons in minimizing the total CST (i.e., maximizing translational
efficiency) is to use isoaccepting tRNAs in proportion to their
concentrations (see Materials and Methods). That is, pi = qi, where
pi is the relative usage of the ith synonymous codon of an amino
acid (Spi = 1) and qi is the relative concentration of the
corresponding tRNA (Sqi = 1). Under this codon usage, available
cognate tRNAs of synonymous codons have equal concentrations
and synonymous codon selection times become identical (see
Materials and Methods). We will refer to this theoretical optimal
codon usage under tRNA shortage as the proportional rule. The
proportional rule is not predicted by other models. For example,
without tRNA shortage, two optimal solutions in minimizing the
total CST exist. When codon usage is fixed, isoaccepting tRNA
concentrations should follow q2i =q2j ~pi =pj , which is referred to as
the square rule [30–31]. When tRNA concentrations are fixed,
only the codon corresponding to the most abundant tRNA species
should be used [30], which is referred to as the truncation rule.
To test if the actual codon usage of yeast follows the
proportional rule, we examined the 12 amino acids that are each
translated by at least two tRNA species in yeast. For each amino
acid, the relative transcriptomic usage of a codon among
synonymous codons (i.e., pi) is quite close to the relative gene
copy number of its cognate tRNA among isoaccepting tRNAs (i.e.,
qi), as predicted by the proportional rule (Figure 2A). We measured
the Euclidian (Figure 2B) and Manhattan (Figure 2C) distances in
synonymous codon usage from the observed values to those
predicted by the proportional rule, and found these distances
significantly shorter than expected by chance (Figure 2B–2D;
Table S1; see Materials and Methods). Not surprisingly, genomic
codon usage fits the proportional rule less well than the
transcriptomic codon usage (Figure 2A), reflected by greater
distances from the predicted values (Figure 2B, 2C).
The better fitting of the transcriptomic codon usage to the
proportional rule than to the square rule and truncation rule can
be seen from a comparison of the distances under these three
models (Figure 2D). We also compared the likelihood of the three
models, given the observed codon usage (Figure 2D). The
proportional model has a much higher log10(likelihood) than the
square model. Because the likelihood of the truncation model is 0,
this model is much worse than the other two models. The same
conclusions are reached for the transcriptomic codon usage of all
other model eukaryotes we examined (Figure 2A, 2D).
In the above analysis, we combined synonymous codons that
are recognized by the same tRNA species (referred to as isosynonymous codons). Because the relative usage of such isosynonymous codons does not affect the relative usage of
isoaccepting tRNAs, it presumably does not affect translational
efficiency. Nonetheless, iso-synonymous codons are not used
equally, and factors other than translational efficiency (e.g.,
translational accuracy) may be at work (Table S2).
Codon–tRNA imbalance reduces translational efficiency
The observation of similar CSTs among synonymous codons
and the empirical validation of the proportional rule strongly
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Figure 2. Synonymous codons are used in proportion to cognate tRNA concentrations. (A) Relative uses of synonymous codons in the
transcriptomes of seven model eukaryotes are compared to the relative concentrations of cognate tRNAs measured from gene copy numbers, for the
12 amino acids that have at least two isoaccepting tRNA species. For comparison, genomic synonymous codon usage in S. cerevisiae is also
presented. The diagonal line shows the predicted proportional relationship between tRNA concentrations and cognate codon uses that maximizes
translational efficiency under tRNA shortage. (B) Euclidian and (C) Manhattan distances between the observed synonymous codon usage in S.
cerevisiae and
the prediction
by the proportional rule are significantly smaller than chance expectations. Euclidian and Manhattan distances are
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
u k
k
X
uX
(pi {qi )2 and
Dpi {qi D, respectively, where pi and qi are codon and cognate tRNA fractions, respectively, and k is the number of
defined by t
i~1
i~1
different tRNA species for the amino acid concerned. The chance expectations are shown by the frequency distributions of the distances under
uniformly random codon usage, determined from 106 simulations. (D) Euclidian and Manhattan distances between the observed synonymous codon
usage and the predictions under the proportional rule, square rule, and truncation rule, respectively. P values indicate the probability that a distance
generated by random codon usage is smaller than the observed distance, determined by 106 simulations. Log10(likelihood ratio) measures the
likelihood of the proportional rule, relative to the square rule, given the actual codon usage.
doi:10.1371/journal.pgen.1002603.g002
The above results were not due to different random mutations
fixed in the genomes of the four strains during our experiments,
because the vYFP signals were not significantly different among
the strains upon removal of the plasmids (Figure 3E). We also
sequenced the entire plasmid DNA from each strain and found no
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mutation. Using quantitative polymerase chain reaction, we
further verified that the vYFP mRNA abundance is not different
among the four strains (Figure 3F). Thus, the among-strain
variation in vYFP signal must be due to a variation in translation.
We also confirmed our results by a finer control of mCherry
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Figure 3. Experimental evidence for the impact of codon usage imbalance on translational efficiency. (A) Experimental design for
examining the impact of mCherry expression on the expression of the reporter vYFP. An mCherry gene is constitutively expressed from a 2-micron
plasmid in S. cerevisiae, whereas vYFP is constitutively expressed from Chromosome XII. Four different synonymous versions of mCherry are
compared. (B) The codon adaptation indices (CAIs) of the four synonymous mCherry sequences (circled numbers), in comparison to CAIs of all S.
cerevisiae genes. (C) Values of distance to native codon usage of yeast (Dncu) for the four mCherry sequences, in comparison to that of all S. cerevisiae
genes. (D) Relationship between vYFP expression and the CAI or Dncu of mCherry, when the mCherry expression is controlled for. A finer control of
mCherry expression is presented in Figure S6, where cells of the low, intermediate, and high mCherry expressions defined here are each subdivided
into 5 bins. Error bars, which are barely seen, show one standard error. (E) vYFP expressions in the four strains after the removal of the plasmids that
carry mCherry. Error bars show one standard error. (F) vYFP mRNA levels of the four strains relative to that of the wild-type strain, which does not carry
mCherry. The mean expressions from three biological replications and the standard errors are presented.
doi:10.1371/journal.pgen.1002603.g003
expression and ruled out the possibility that our observation is a
byproduct of potential differences in translational accuracy among
different mCherry versions (Figure S7; see Materials and Methods).
Furthermore, because the accuracy hypothesis is based on CAI and
thus predicts a higher vYFP expression in the strain carrying
mCherry-4 than that carrying mCherry-3, our results (Figure 3D) are
inexplicable by this hypothesis. Similarly, mechanisms resulting
from translational errors, such as protein misfolding or aggregation, cannot explain our observation either.
In the experiment, we used vYFP to represent native genes in the
yeast genome. However, because vYFP and mCherry have 71/
220 = 32% of protein sequence identity, one might ask whether
our observation can be generalized. Specifically, could the
negative influence of mCherry expression on vYFP expression
be caused entirely by the similarity in codon usage between
mCherry and vYFP? We measured the codon usage dissimilarity
between a pair of genes by a Euclidian distance and examined the
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distribution of this distance between each mCherry version and all
yeast genes (Figure S8). The distribution is approximately bell
shaped and the distance between mCherry and vYFP falls in the
central part of the bell, suggesting that mCherry is no more similar
to vYFP in overall codon usage than to average yeast genes.
Furthermore, our results cannot be explained by amino acid
similarity between mCherry and vYFP, because all mCherry
versions have the same amino acid sequence and should not
differentially affect vYFP expression through amino acid usage.
Thus, our observation from vYFP can be extrapolated to native
genes in the yeast genome.
Why more highly expressed genes have stronger CUB
If translational efficiency is maximized when the cellular codon
usage follows the proportional rule, why do highly expressed genes
necessarily prefer codons with highly abundant cognate tRNAs
and have stronger CUB than lowly expressed genes? We
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If amino acid frequencies are in perfect proportion to tRNA
concentrations, the mean CST for an amino acid should not vary
among amino acids. This uniformity, however, is not observed in
yeast (Figure S12), suggesting that amino acid usage is only
roughly proportional to tRNA concentrations (Figure 5A), which
may be due to mutational bias [37] or antagonistic selective
pressures from factors such as physiochemical properties [38] and
synthetic costs [39] of various amino acids. Our model predicts
that the average CST of an amino acid increases with the decrease
of the relative availability of tRNAs for the amino acid. Indeed, a
negative correlation exists between the tRNA availability and CST
for the 20 amino acids (Pearson’s r = 20.40, P = 0.03, permutation
test; Figure 5B). This finding reconfirms tRNA shortage in
translation, explains in part why CSTs of nonsynonymous codons
vary, and indicates compromised translational efficiency due to
other fitness effects of amino acid usage.
hypothesize that these phenomena are due to differential selective
coefficients associated with synonymous mutations occurring in
highly expressed and lowly expressed genes in the regain of the
codon-tRNA balance upon a genetic perturbation. Let us imagine
an amino acid with two synonymous codons (codon1 and codon2)
that each uses a distinct tRNA species (tRNA1 and tRNA2) and
assume that the present codon usage follows the proportional rule.
Now, if the proportion of tRNA1 rises due to a mutation, natural
selection will promote the fixations of synonymous mutations from
codon2 to codon1 to reestablish the codon-tRNA balance. Such
advantageous mutations occurring in highly expressed genes affect
tRNA usage more than those occurring in lowly expressed genes
and hence have a greater selective advantage and are fixed faster.
This difference becomes even bigger when clonal interference [34]
is considered. As a result, highly expressed genes use more codon1
and fewer codon2 than before and show stronger CUB. The
contrasting scenario, in which the tRNA usage is rebalanced by
frequent use of codon1 in lowly expressed genes, requires many
synonymous substitutions in many lowly expressed genes, which
will not happen because it takes much longer than rebalancing the
tRNA usage by increasing codon1 frequency in highly expressed
genes. Indeed, in a computer simulation of codon usage evolution
that starts from the equal usage of 4 synonymous codons whose
cognate tRNAs have different concentrations, the final usage of
the codons, after 500 generations of random mutation, genetic
drift, and natural selection for translational efficiency, follows the
proportional rule (Figure 4A). More importantly, the preferential
use of high-concentration tRNA species and strong CUB in highly
expressed genes are seen from both the average of 1000 simulation
replications (Figure 4B) and any one replication (Figure 4C). The
standard deviations presented in Figure 4B indicate an extremely
low probability for CUB to be stronger or a preferred codon to be
used more frequently in lowly expressed genes than highly
expressed genes. As expected, the phenomena in Figure 4
disappear when the natural selection for translational efficiency
is removed in the simulation (Figure S9). These observations
support our model that the high CAI of highly expressed genes is a
byproduct of natural selection for an overall cellular efficiency in
translation, rather than the direct product of stronger selection for
translation efficiency in more highly expressed genes [6].
Discussion
The translational efficiency hypothesis of CUB
Results from several earlier experiments are consistent with the
role of CUB in enhancing translational efficiency or reducing
ribosome sequestering [12,17]. For example, when expressing
many synonymous versions of a green fluorescent protein (GFP)
gene in E. coli, Kudla and colleagues reported that strains
harboring high-CAI GFP genes tend to grow faster than those
harboring low-CAI GFP genes, despite the lack of a correlation
between the GFP protein expression level and its CAI [12].
Although these authors found no correlation between CAI and
protein misfolding, their experiment was unlikely to be sensitive
enough for quantifying GFP misfolding [12]. Thus, it could not
rule out the possibility that the observed variation in fitness was
entirely caused by CUB’s influence on translational accuracy. By
contrast, we were able to demonstrate CUB’s impact on
translational efficiency after excluding its impact on translational
accuracy.
A recent study in E. coli showed that the ribosome shortage
induced by over-expression of unneeded proteins can be alleviated
by physiological adaptation in 30 to 40 generations, owing to the
manufacture of additional ribosomes [40]. This finding suggests
that the disadvantage of suboptimal codon usage may also be
mitigated by physiological adaptation. Nevertheless, physiological
adaptation takes time. If the growth rate fluctuates rapidly due to
frequent environmental changes, the fitness of the individual with
suboptimal codon usage is expected to be much lower than the
individual with balanced codon usage.
We hypothesized and demonstrated that translational efficiency
is optimized by codon-tRNA balance. This new model of
translational efficiency by unequal codon usage differs substantially from the prevailing model (Table 1). One critical piece of
evidence for our model is similar CSTs of synonymous codons in
wild-type yeast. Our CST estimation is based on the assumption
that the time a codon occupies the ribosomal A site equals the
waiting time for the cognate tRNA. Our estimates of all CSTs
would be biased upward to a similar level if downstream ‘‘traffic
jams’’ happen during translational elongation. However, a recent
study suggested that downstream traffic jams are unlikely, due to
slow ‘‘ramps’’ at the beginning of an mRNA [21]. Furthermore,
even if downstream traffic jams occur, it should affect synonymous
codons as well as nonsynonymous codons and thus cannot explain
why only synonymous codons but not nonsynonymous codons
have similar CSTs.
Over two decades ago, Curran and Yarus indirectly estimated
relative CSTs for 29 sense codons in E. coli, under the assumption
Optimal amino acid usage under tRNA shortage
Analogous to synonymous codon usage, we predict that the
optimal amino acid (or nonsynonymous codon) usage in speeding
up translation is in proportion to the corresponding tRNA
concentrations. Indeed, amino acid frequencies inferred from
transcriptome data were reported to correlate positively with the
corresponding tRNA gene copy numbers in yeast [35] and C.
elegans [36]. More importantly, actual amino acid usage is
significantly closer than random usage to our predicted optimal
(i.e., the diagonal line in Figure 5A; P,1026, simulation test). This
phenomenon is also true in all other model eukaryotes examined,
although the level of match between the observation and
prediction varies among species (Figure 5A). Transcriptomic
amino acid usages instead of proteomic amino acid usages are
plotted here because the latter are unavailable for most species.
Nevertheless, S. cerevisiae data showed an almost perfect correlation
between transcriptomic and proteomic amino acid usages (Figure
S10), indicating that the former is a good proxy for the latter. We
also predict a positive correlation between aminoacyl tRNA
synthetase concentration and corresponding tRNA concentration
to enhance the efficiency of amino acid charging. Such a
correlation is indeed found in S. cerevisiae (r = 0.45, P = 0.03; Figure
S11).
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Codon Usage and Translational Efficiency
mutations, genetic drift, and natural selection for translational efficiency
are considered (see Materials and Methods). (A) Overall changes of
transcriptomic codon usage averaged from 1000 simulation replications. Error bars show one standard deviation. (B) Highly expressed
genes evolved stronger codon usage biases than lowly expressed
genes. The averages from 1000 simulation replications are presented.
Error bars show one standard deviation. (C) Evolutionary changes in the
usage of codon4, the codon recognized by the most abundant tRNA, in
a randomly chosen simulation replication.
doi:10.1371/journal.pgen.1002603.g004
that the probability of a frame shift in the translation of a codon is
proportional to the CST of the codon [41]. They reported that
only codons of very low CSTs tend to be preferentially used [41].
However, because their fundamental assumption about the frameshift rate is incorrect [42], their CST estimates are unlikely to be
correct. It is also possible that prokaryotes and eukaryotes have
some differences in using CUB to regulate translational efficiency
(e.g., translational attenuation in prokaryotes). In another E. coli
study, Sorensen and colleagues reported faster translation of a
multicopy-plasmid-borne lacZ gene when a segment of the gene
comprises mainly preferred codons than when it comprises mainly
unpreferred codons [43]. This result cannot be used to infer
relative CSTs of synonymous codons in wild-type cells, because the
extremely high expression of synonymous versions of the
endogenous lacZ gene from plasmids potentially breaks the
codon-tRNA balance and alters CSTs. Nevertheless, their
observation is fully compatible with our finding of different levels
of translational efficiency induced by the expressions of different
synonymous versions of mCherry. Several other studies reported
similar findings [25,44]. Recently, some authors calculated CSTs
by assuming that the CST of a codon is determined by the relative
concentrations of its cognate, nearly cognate, and non-cognate
tRNAs without considering tRNA shortage or using ribosome
profiling data [45]. Because of the violation of the fundamental
assumption they made, their estimates are likely to be incorrect.
Indeed, their estimated CSTs would predict a slower translation of
mCherry version 3 than 4, contradictory to our experimental
result (Figure 3D). While the present work was under review,
Ingolia and colleagues reported estimates of translational elongation speeds in mouse embryonic stem cells using a pulse-chase
strategy that does not involve expressions of heterologous genes
[46]. Although their method is different from ours, their finding of
similar elongation speeds among synonymous codons is highly
consistent with our results from yeast.
Our discoveries require reinterpretation of several earlier
observations. For example, higher prevalence of codons with
abundant cognate tRNAs in genes with higher expressions is often
interpreted as a result of a stronger demand for fast translation of
more abundant proteins [19–20]. This interpretation is not
supported by our results. Rather, we suggested and demonstrated
by simulation that, the selection coefficient for synonymous
mutations that help achieve the codon-tRNA balance is greater
in highly expressed genes than in lowly expressed genes, leading to
quicker and more acquisitions of codons with abundant cognate
tRNAs in the former than in the latter. In this regard, our results
support that CUB serves as a global strategy to enhance the
efficiency of the translation system [12,47].
Within an organism, the transcriptome can vary among cell
cycle stages, developmental stages, and tissues. How do such
variations affect the codon-tRNA balance? We found pairwise
Pearson’s correlations in transcriptomic usage of all 61 sense
codons to be nearly 1 among different time points in the S. cerevisiae
mitotic cell cycle (Figure 6). We further analyzed the transcriptomic usage of all 61 codons across tissues and/or developmental
Figure 4. Computer simulation demonstrates that selection for
translational efficiency results in the preferential use of codons
with abundant cognate tRNAs in highly expressed genes. Ten
genes with different expression levels are considered for a haploid
organism. Four synonymous codons of an amino acid are each
recognized by its cognate tRNA. Concentrations of the four tRNAs
differ, but the initial codon frequencies are equal. Synonymous
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Codon Usage and Translational Efficiency
Figure 5. Amino acids are used approximately in proportion to cognate tRNA concentrations. (A) Relative uses of amino acids estimated
from the transcriptomic data of 7 model eukaryotes are compared to the relative concentrations of their cognate tRNAs measured from gene copy
numbers. The diagonal line shows the predicted proportional relationship between tRNA concentrations and cognate amino acid frequencies that
maximizes translational efficiency under tRNA shortage. PE (or PM) is the probability that the Euclidian (or Manhattan) distance between the amino
acid usage randomly generated under a uniform distribution and that predicted by the proportional rule is smaller than the observed distance, and is
estimated from 106 simulations. The distance definitions are the same as those in the legend of Figure 2, except that i is an amino acid instead of a
codon. (B) The average CST of an amino acid in S. cerevisiae is negatively correlated with the availability of its cognate tRNAs, which is measured by
the fraction of cognate tRNA genes among all tRNA genes divided by the frequency of the amino acid estimated from the transcriptome. The P-value
is determined from 1000 permutations of CSTs.
doi:10.1371/journal.pgen.1002603.g005
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Codon Usage and Translational Efficiency
Table 1. Comparison between the old and new models of translational efficiency by unequal codon usage.
Comparisons
Old model
New model
Ternary complexes of aminoacylated
tRNA+eEF-1á+GTP
In excess.
In shortage.
Translational speeds of synonymous
codons in wild-type cells
Faster for those with higher cognate tRNA
concentrations.
Equal, because codon usage has been optimized to
be proportional to cognate tRNA concentrations.
Translational speeds of synonymous
codons in mutant cells
Faster for those with higher cognate tRNA
concentrations.
Unequal when the codon-tRNA balance is broken.
Slower for codons with higher ratios between the
codon fraction and the cognate tRNA fraction.
Why is the codon usage bias stronger
in more highly expressed genes?
Fast translation of highly expressed genes is favored
over fast translation of lowly expressed genes.
Synonymous mutations in highly expressed genes
have larger effects than those in lowly expressed
genes in regaining the codon-tRNA balance, which
increases the overall translational efficiency of the cell.
Why is the codon usage proportional
to cognate tRNA concentration?
No explanation.
It maximizes the overall cellular translational
efficiency when ternary complexes are in shortage.
How to reach the highest cellular translational
efficiency in making a synthetic cell?
Exclusive use of preferred codons.
Codon usage in proportion to cognate tRNA
concentrations.
doi:10.1371/journal.pgen.1002603.t001
our result invalidates assumption (ii) for wild-type cells, the above
argument no longer holds. Thus, even though translational
accuracy may be affected by relative concentrations of tRNAs in
engineered yeast cells with grossly imbalanced codon-tRNA usage
[51], this impact is not expected in wild-type cells because our
results strongly suggest that isoaccepting tRNA species have
effectively the same concentrations in wild-type cells. In addition,
the enrichment of preferred codons at evolutionarily conserved
amino acid residues cannot be explained by the translational
efficiency hypothesis [7–10]. Furthermore, experimental data
showed that translational accuracies of iso-synonymous codons
vary [52], suggesting that the variation in accuracy cannot be
entirely caused by the variation in cognate tRNA concentration,
because iso-synonymous codons use the same cognate tRNA.
Rather, comparative genomic analyses strongly suggest that
translational accuracy is likely to be intrinsically different among
synonymous codons [1,53]. Further, we were able to establish
CUB’s impact on translational efficiency even after we controlled
its impact on translational accuracy (Figure 3, Figure S7). In
addition, because translational accuracy is not entirely determined
by translational efficiency [7–10], the proportional rule, which is
predicted from selection for efficiency, is not predicted from
selection for accuracy, especially because translational errors at
different residues have different fitness effects. Thus, the impact on
efficiency cannot be a byproduct of the impact on accuracy. Taken
together, we conclude that translational accuracy and efficiency
are two separable benefits of CUB.
stages in the worm, fruitfly, and human. If multiple replications of
the same cell type exist in a dataset, we randomly chose one
replication in our analysis. Similarly high correlations were
observed among different cell types within species (Figure 6). By
contrast, the correlation is generally below 0.5 between any pair of
the four species examined here. The high correlation in codon
usage across cell cycle stages, developmental stages, and tissues of
the same species is likely due to house-keeping genes, which are
always highly expressed. Thus, within-organism gene expression
variations have little impact on the maintenance of the codontRNA balance. Further, tRNA concentrations may covary with
the transcriptomic codon usage to maintain the codon-tRNA
balance across tissues [48].
A byproduct of our CST estimation is the translational initiation
rate of each gene. We found that the translational initiation rate is
significantly positively correlated with the mRNA concentration
(r = 0.34, P = 6610281), suggesting a coordinated regulation of
gene expression at the transcriptional and translational levels. We
also observed a strong positive correlation between the translational initiation rate and CAI (r = 0.51, P,102196), suggesting that
CAI provides a moderate amount of information about the
translational initiation rate. This may explain why the protein
concentration correlates with the product of mRNA concentration
and CAI better than with the mRNA concentration alone [49].
Several studies revealed reduced mRNA stability near the
translation initiation site, suggesting that the reduced stability
may enhance the translational initiation rate [12,32–33]. Indeed,
we found a weak but significant positive correlation between the
reduction in mRNA stability [32] and our estimated translational
initiation rate (r = 0.08, P = 161025).
Evolutionary models of codon usage bias
Let us compare three evolutionary models of CUB that differ in
the roles of translational accuracy and efficiency as the selecting
agent. We also consider mutational bias and genetic drift, two
known factors in the evolution of CUB, in these models. In model
I, translational efficiency is the sole selecting force (Figure 7). This
model predicts co-evolution of codon usage and cognate tRNA
concentrations and a codon-tRNA balance at which the relative
frequency of a synonymous codon (pi) equals the relative
abundance of its cognate tRNA (qi). The expected values of pi = qi
are determined by the mutational bias, which directly affects
codon usage and indirectly affects tRNA concentrations. However,
this model cannot explain the observation that, although preferred
codons of an amino acid vary among species, this variation
Translational efficiency and accuracy are two separable
benefits of CUB
Given that CUB improves both translational efficiency and
accuracy, one wonders whether one of these effects is a side-effect
of the other. For instance, it was previously suggested that the
variation in translational accuracy among synonymous codons
may be a byproduct of the variation in translational efficiency,
because (i) most translational errors are believed to occur during
codon selection, (ii) codon selection has been assumed to be faster
for preferred codons than unpreferred codons, and (iii) faster
codon selection is thought to result in fewer errors [50]. Because
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Codon Usage and Translational Efficiency
Figure 6. Similarity in transcriptomic codon usage across cell cycle stages, developmental stages, and tissues. Distributions of pairwise
Pearson’s correlations of codon usage among (A) mitotic cell cycle stages in S. cerevisiae, (B) developmental stages in C. elegans, (C) tissues and
developmental stages in D. melanogaster, and (D) among tissues in H. sapiens.
doi:10.1371/journal.pgen.1002603.g006
decreases substantially (but does not disappear) after the control of
genomic GC content [1]. For example, GTT and GTA both code
for valine and have the same GC content, but GTT is frequently
used as the preferred codon when the genomic intergenic GC
content is below 50% [1]. When the GC content exceeds 50%,
GTG rather than GTC is often used as the preferred codon for
valine [1]. This observation suggests that, in addition to
translational efficiency, there is a separate selecting force with a
relatively constant direction.
In model II, translational accuracy is the sole selecting agent on
CUB (Figure 7). The demand for translational accuracy, coupled
with the mutational bias, determines the expected CUB, whereas
selection for translational efficiency determines tRNA concentrations based on codon frequencies. The phenomenon of stronger
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CUB in more highly expressed genes is explainable by the proteinmisfolding-avoidance hypothesis which predicts that highly
expressed genes are translated more accurately by using accurate
codons more frequently [7,54]. Model II predicts that, after the
control for the mutational bias, accurate codons are always the
preferred codons in a species. If the translational accuracy of a
codon is an intrinsic property of the codon and does not vary
among species [29], we should observe no variation in the choice
of preferred codons, after the control of mutational bias. This
prediction, however, is incorrect, because preferred codons are not
always the same in different species with the same mutational bias
[1,29]. A more rigorous test of this model is to compare the
accurate and preferred codons of each amino acid in a species,
because model II predicts a complete match between them. For
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Codon Usage and Translational Efficiency
Figure 7. Evolutionary models of synonymous codon usage bias. Three models that differ in the involvement of natural selection for
translational accuracy and efficiency in the evolution of codon usage bias. Models I and II can be rejected by the existing data, whereas model III is
supported by available data.
doi:10.1371/journal.pgen.1002603.g007
each codon, we calculated an odds ratio by the relative use of the
codon over other synonymous codons at conserved amino acid
positions divided by that at non-conserved amino acid positions;
the synonymous codon with the highest odds ratio is regarded as
the most accurate codon because it is most preferentially used at
important amino acid positions [7–10]. By comparing S. cerevisiae
with its relative S. bayanus, we identified conserved and nonconserved amino acid positions. We calculated the odds ratio for
each codon in each gene and then combined the odds ratios from
all genes using the Mantel-Haenszel procedure [23]. By definition,
the preferred codon of an amino acid is the one with the highest
RSCU’. We found that, in 6 (Ala, Asp, Gly, His, Thr, and Val) of
the 18 amino acids that have at least two synonymous codons, the
codon with the highest odds ratio is different from the codon with
the highest RSCU’ (Figure 8). Furthermore, for three amino acids
(Asp, His, and Thr), the codon with the highest RSCU’ has an odds
ratio significantly lower than 1 (Figure 8). We also used the 10%
most highly expressed genes to calculate odd ratios; 8 (Ala, Arg,
Asp, Cys, Ile, Leu, Thr, and Val) of the 18 amino acids show
mismatches between the codon with the highest RSCU’ and the
codon with the highest odds ratio (Figure 8). These results provide
unambiguous evidence for the inadequacy of model II.
In model III, selections for translational accuracy and efficiency
jointly determine CUB (Figure 7). Let us consider three types of
synonymous mutations with regard to their impacts on translational accuracy and efficiency. First, a synonymous mutation is
likely to be fixed when it enhances both translational accuracy and
efficiency, but is likely to be lost when it decreases both. Second, a
synonymous mutation may increase the accuracy but reduce the
efficiency. One possible outcome is that selection for higher
accuracy will gradually alter the codon usage, which is followed by
tRNA concentration changes that recover the loss of efficiency.
Eventually, accurate codons will be the preferred codons.
Alternatively, selection for higher accuracy may not be able to
alter the codon usage permanently if the loss of efficiency is either
too large or cannot be recovered by a corresponding tRNA change
as quickly as the switch back of the codon usage. Consequently,
accurate codons cannot become the preferred codons and the
system is trapped in a local fitness peak that is the maximum for
efficiency but not accuracy. For example, while codon CCA is
more accurate than CCT for proline (Figure 8), there are still
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about a quarter of bacterial species with GC%,40 that use CCT
as their preferred proline codon [1], suggesting that it is not rare
for codon usage to be trapped in a local fitness peak. Third, a
synonymous mutation may increase the efficiency but reduce the
accuracy when the system is at a codon-tRNA imbalance.
Although the fate of this mutation is determined by the relative
strengths of the two forces, changes of tRNA concentrations could
resolve the conflict better because they can increase efficiency
without reducing accuracy. So, the final codon usage pattern will
also depend on the rate of mutations that alter tRNA
concentrations. While the quantitative aspects of model III require
further exploration, it is clear that the model is able to explain, at
least qualitatively, both the matches and mismatches between the
accurate and preferred codons (Figure 8). It is also able to explain
the codon-tRNA balance and the phenomenon of stronger CUB
in genes with higher expressions. Thus, model III is most
compatible with and best supported by available data. In addition
to translational accuracy and efficiency, synonymous codon usage
of individual genes may also be shaped by other forces, for
example, those related to RNA splicing and stability [55]. But
these forces are gene-specific and do not create genomic patterns
of CUB.
Implications for synthetic biology
Synthetic biology designs and constructs novel biological
functions not found in nature. It has long been known that, in
many but not all cases, increasing the CAI of a transgene boosts its
protein expression [12,56–57]. Different protein expression levels
of synonymous transgenes are likely caused by CST differences
created by various degrees of codon-tRNA imbalance induced by
transgene expressions. Consistent with this idea, overexpression of
rare tRNAs of E. coli (the bio-reactor) can rescue the tRNA
depletion when heterologous human genes are expressed in E. coli
[56]. When an artificially designed gene is added to a host cell, the
potential imbalance between the overall cellular codon usage and
the tRNA pool also affects the expressions of native genes and
hence the growth of the host cell. We showed that Dncu, a newly
devised index measuring the distance in codon usage between the
transgene and the host cell, is an accurate indicator of the impact
of per transgene protein molecule production on the expressions of
native genes. We demonstrated that it is the Dncu rather than CAI
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Codon Usage and Translational Efficiency
Figure 8. Matches and mismatches between preferred codons and accurate codons in S. cerevisiae. Odds ratio (bars) measures the
enrichment of a synonymous codon at evolutionarily conserved amino acid residues relative to that at non-conserved residues and is used as a proxy
for translational accuracy. RSCU’ (orange dots) measures the preference in codon usage. Odds ratios are estimated from either all genes (black) or the
10% most highly expressed genes (grey) of S. cerevisiae. Asterisks indicate 5% significance in the deviation of an odds ratio from 1 (uncorrected for
multiple testing).
doi:10.1371/journal.pgen.1002603.g008
tRNA gene copies in the above species were obtained from the
genomic tRNA database (http://lowelab.ucsc.edu/GtRNAdb/).
of the transgene that predicts its impact on the host protein
expression. Therefore, Dncu should be considered in synthetic
biology when the impact of transgene expression on host gene
expressions is a concern. Further, when genes from multiple
species are assembled into a synthetic genome, designing tRNA
gene numbers in proportion to the usage of their cognate codons
will likely make protein expressions in the entire cell most efficient.
Estimation of codon selection time (CST)
Using the S. cerevisiae ribosome profiling data [18], we identified
codons docked at the ribosomal A site, from the Illumina Genome
Analyzer sequencing reads. By comparing the observed codon
frequencies in the ribosome profiling data with the expected codon
frequencies estimated from mRNA-Seq data generated under the
same condition in the same experiment, we calculated the relative
CSTs of all 61 sense codons. Although Illumina sequencing may be
biased toward certain sequences or nucleotides [64], this bias
affects the mRNA-Seq and ribosome profiling data equally and
thus will not affect our estimation of CST. For a sequencing read
from the ribosome profiling data, nucleotide positions 16–18 were
considered to be at the ribosomal A site where codon selection
occurs [18]. Only those reads with exactly 28 nucleotides and 0
ambiguous sites were used to ensure the accurate determination of
positions 16–18. We calculated the fraction of in-frame codons by
comparing the read sequences with annotated yeast coding
sequences. Consistent with what was previously reported [18],
the majority of codons at positions 16–18 were in-frame in the
ribosome profiling data. In the mRNA-Seq data, the fraction of
each phase was close to one third, as expected. All out-of-frame
codons were excluded. The probability of incorrect codon
assignment was low, because only codons misaligned by at least
3 nucleotides may be assigned incorrectly. Transposons and
uncharacterized genes were removed. Our CST estimation
procedure (Figure S1) is as follows.
We first calculated fi, the observed frequency of codon i, in the
ribosome profiling data by
Materials and Methods
Yeast genomic data
The yeast ribosome profiling data [18] were downloaded from
Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/) under
accession number GSE13750. Gene expression and protein
expression levels were from http://web.wi.mit.edu/young/
expression/ [58], http://www.imb-jena.de/tsb/yeast_proteome/
[59], and the supplementary data of a previous study [60].
Transcriptomic data for the yeast mitotic cell cycle were from a
previous study [61]. Gene sequences and reading frames were
downloaded from Saccharomyces Genome Database (SGD, www.
yeastgenome.org). Numbers of tRNA gene copies were retrieved
from an earlier study [22].
Genomic data of other eukaryotes
Gene expression levels in A. thaliana, D. melanogaster, M. musculus,
and H. sapiens were downloaded from Gene Expression Omnibus
(GDS416, GDS2784, GDS592 and GDS596, respectively). Gene
expression levels in S. pombe and C. elegans were retrieved from two
earlier studies [62–63], respectively. Peptide and cDNA sequences
of S. pombe, A. thaliana, C. elegans, D. melanogaster, M. musculus, and H.
sapiens were from Ensembl (www.ensembl.org/). Numbers of
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Codon Usage and Translational Efficiency
N
X
fi ~
initiation rates (uniform, proportional to CAI of each gene, inversely
proportional to CAI) were used in CST estimation and they resulted
in identical estimates of CSTs (Figure S3A, S3B). Thus, CST
estimation does not depend on the initial values of R. The standard
errors of the CST estimates were estimated by bootstrapping genes
present in the ribosomal profiling data 1000 times. The CST
estimates from two different media (rich and starvation) are also
very similar (Figure S3C). To ensure no mistake in the estimation of
CST, the first two authors of this paper independently derived the
formulas, wrote the computer programs, and estimated the CSTs,
and their results were virtually identical.
cij
j~1
61 X
N
X
ð1Þ
,
cij
i~1 j~1
where cij is the count of codon i in mRNA j positioned at the
ribosomal A site measured by ribosome profiling and N is the
number of genes with ribosome profiling data (N.3000 for both
rich and starvation conditions). The expected ribosome footprint
frequencies of codon i (Fi) when all codons have equal CST can be
calculated based on the frequency of the codon in the mRNA-Seq
data using
N
X
Fi ~
Estimation of synonymous codon usage bias in yeast
There are two commonly used measures of synonymous codon
usage bias. The first is the relative synonymous codon usage (RSCU),
defined by the frequency of a codon relative to the average
frequency of all of its synonymous codons in a set of highly
expressed genes [19]. Codons with RSCU.1 are preferred and
those with RSCU,1 are unpreferred. To compare the usage of all
61 sense codons, we also used RSCU’ = RSCU/n, where n is the
number of synonymous codons of an amino acid. RSCU’ of a
codon is the proportion of use of a given codon among
synonymous choices in a set of highly expressed genes. The
second commonly used measure of synonymous codon usage bias
is the codon adaptation index (CAI), which is calculated for a gene, and
measures its usage of high-RSCU codons [20]. Briefly, CAI of a
gene is the geometric mean of RSCU divided by the highest
possible geometric mean of RSCU given the same amino acid
sequence. CAI is a positive number no greater than 1. The greater
the CAI, the more prevalent are preferred codons in the gene.
We first selected 200 most highly expressed genes based on a
previous study [59]. Sixteen of these genes did not have expression
information in another study [58] and 4 had expression levels lower
than 4 times the genomic average (2.7 mRNA/cell reported in an
earlier study [58]). The remaining 180 highly expressed genes were
used to calculate RSCU and RSCU’ for each codon. Our RSCU
estimates were highly correlated with those previously reported [20]
(r = 0.995, P,0.001, permutation test). CAI was calculated for each
yeast gene and for each version of mCherry based on the RSCU values
obtained above, following a previous study [20].
We also estimated the effective number of codons (Ncp) for each
gene, after controlling the GC content of the gene [65–66]. We
separately estimated the frequency (f) of each of the 61 sense
codons in each gene. We then estimated Spearman’s rank
correlation (r) between Ncp and f among all genes for each codon.
Among synonymous codons, those with more negative r values
are considered to be more preferred [1]. This dataset was used in
Figure S4 only.
(Rj Cij )
j~1
61 X
N
X
,
ð2Þ
(Rj Cij )
i~1 j~1
where Rj is the translational initiation rate of mRNA j and Cij is the
count of codon i in mRNA j measured by mRNA-Seq. Then, the
relative codon selection time for codon i is calculated by
ð3Þ
CSTi ~fi =Fi :
We used an iterative approach to estimate the translational
initiation rates that appear in Eq. 2. We first used Rj = 1 for all j.
After the CST is calculated for each codon, the elongation rate ej of
mRNA j (i.e., the number of codons translated per unit time) is
calculated by
ej ~
Lj
61
X
,
ð4Þ
(Dij CSTi )
i~1
where Lj is the number of codons in each molecule of mRNA j and
Dij is the number of codon i in each molecule of mRNA j. The
translational initiation rate Rj can be estimated from
ð5Þ
Rj ~ej dj ,
where dj is the ribosome density on mRNA j (i.e., the number of
ribosomes per codon) and can be estimated by
61
X
dj ~
i~1
61
X
Concentrations of ternary complexes in E. coli
cij
:
It has been reported that the physiological concentration of the
ternary complex is ,200 nM for Phe tRNA and Lys tRNAs in E.
coli [67]. Because the number of Phe tRNA and Lys tRNA
molecules per cell is 1830 and 4300, respectively [68], we
calculated that the Phe tRNA concentration is 1830/(6.0261023)/
(1.1610215) = 2.861026 M = 2800 nM, where 6.0261023 is the
number of molecules per mole and 1.1610215 liter is the average
volume of an E. coli cell. Similarly, Lys tRNA concentration is
estimated to be 6500 nM. Thus, about 200/[(2800+6500)/2] =
4.3% of tRNAs are in ternary complexes. Because there are
,1.26104 ribosomes per E. coli cell [68], ribosome concentration
is ,18,000 nM. Thus, the ratio in the concentration of ternary
complexes to that of ribosomes is expected to be 200620/
ð6Þ
Cij
i~1
We then used the newly estimated translational initiation rates to
calculate CSTs. After 10 iterations, CST estimates converge (Figure
S2) and are considered as our final estimates. Because our
estimates of CSTs are relative values, we rescaled them by setting
the maximal observed value at 1.
CST estimates from different experimental replicates were highly
correlated (r = 0.79, P = 6610214) and were thus pooled for the rest
of the analysis. Three different sets of initial values of translational
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March 2012 | Volume 8 | Issue 3 | e1002603
Codon Usage and Translational Efficiency
where Yij is the fraction of codon j among the synonymous codons
of amino acid i for the heterologous gene and Xij is the fraction of
codon j among the synonymous codons in the host transcriptome,
ni is the number of synonymous codons for amino acid i. Dncu of
the gene is defined as the weighted geometric mean of Di, or
18000 = 0.22, if Lys and Phe can represent all 20 amino acids in
ternary complex concentration.
Mathematical proof that proportional codon usage
maximizes translational efficiency
Without loss of generality, we assume that an amino acid is
encoded by synonymous codons 1 and 2, which are respectively
recognized by isoaccepting tRNAs 1 and 2. Let the relative usage
of the two codons be p1 and p2 = 12p1 and the relative
concentrations of the two tRNAs be q1 and q2 = 12q1, respectively.
Let the codon selection time for the two synonymous codons be t1
and t2, respectively. Thus, the expected codon selection time for
the amino acid concerned is t = p1t1+p2t2. When tRNAs are in
shortage, the local concentrations of tRNA 1 and 2 are aq1/p1 and
aq2/p2, where a is a constant. Because codon selection time is
proportional to the inverse of the local tRNA concentration, we
p1 b
p2 b
have t~
z
, where b is another constant. The
aq1 =p1 aq2 =p2
above formula can be simplified to t~b(p21 =q1 zp22 =q2 )=a~
(b=a)½1z(p1 {q1 )2 =(q1 q2 ). It is easy to find that t reaches its
minimal value of b/a when p1 ~q1 and p2 ~q2 . In other words, the
expected codon selection time is minimized and thus translational
efficiency is maximized when relative synonymous codon frequencies equal relative tRNA concentrations. Under this condition,
codon selection time equals b/a for both codons and local tRNA
concentration equals a for both tRNAs. A full treatment considering
tRNA cycle and kinetics gave the same result [31].
k
i~1
Yeast experiments
The mCherry gene sequence was obtained from a previous study
[69]. We designed four synonymous DNA sequences encoding the
same mCherry peptide (Figure S5). The first 56 nucleotides were
the same for all four sequences to avoid potential effects on the
mRNA secondary structure, which affects protein translation
[12,32–33]. The GC contents of the four sequences (42–44%)
were also made similar to each other and to the average value in
yeast coding sequences (40%). In all sequences, synonymous
codons were randomized in order and thus were unlikely to cause
differences in order-related effects [27]. The different versions of
mCherry DNA sequences were synthesized by Blue Heron
Biotechnology. They were cloned into p426GPD [70] at SpeI
and XhoI (New England Biolabs; Promega) and are under the
control of the GPD promoter. The plasmids were subsequently
transformed individually into a haploid yeast cell (BY4742) with
vYFP [71] inserted into Chr XII [72]. The genotype of the cell is
MATa his3D1 leu2D0 lys2D0 ura3D0 hoD0::PGPD-Venus.
We measured the expressions of mCherry and vYFP in log
growth phase in Yeast extract/Peptone/Dextrose (YPD) media by
florescence-activated cell scanning (FACSCalibur, BD). Fluorescence of mCherry was measured from FL4 with a 670 nm pass
filter and fluorescence of vYFP was measure from FL1 with a filter
having a 30 nm bandpass centered on 530 nm. Yeast cells with
mCherry fluorescence signals greater than the BY4742 negative
control cells (i.e., mCherry fluorescence signals .10) were gated.
We retrieved the forward scatter (FSC, which is proportional to
cell size) and mCherry and vYFP fluorescence signals for all gated
cells. The expression levels of fluorescent proteins were defined as
their fluorescence signals divided by FSC. The mean mCherry
expression level is 3.38860.002, 6.46860.007, 14.00360.032,
and 14.54460.022 for the strains carrying mCherry-1, 2, 3, and 4,
respectively. Expression levels of mCherry and vYFP were
negatively correlated for each strain (mCherry-1: r = 20.22;
mCherry-2: r = 20.57; mCherry-3: r = 20.60; mCherry-4: r = 20.62;
P,2.2610216 in all cases). All gated cells were then grouped into
3 (Figure 3D) or 15 (Figure S7) bins with equal mCherry
expression ranges. For each genotype, multiple independently
transformed strains were examined on different days, but the
results were highly similar. We thus combined all results obtained
from different strains of the same genotype. The total numbers of
cells measured were 456333, 648792, 352863, and 793832,
respectively, for the strains carrying mCherry-1, 2, 3 and 4
(Figure 3B). To confirm that our results were not due to random
secondary mutations, we removed the plasmids from each strain
by using 59-FOA media to select against the plasmids, and then
measured the vYFP fluorescence intensities. We also sequenced
the entire plasmid DNA from each of the four strains.
We measured the Euclidian distance and Manhattan distance in
synonymous codon usage from the observed values to the values
predicted from the observed tRNA fractions using the proportional
rule. To evaluate whether the observed distances are shorter than
expected by chance, we conducted a computer simulation with 106
replications under random codon usage. That is, the frequency of a
synonymous codon is uniformly distributed between 0 and 1 with the
constraint of the total frequency of all synonymous codons being 1.
We then obtained the distribution of the distance between a random
codon usage and the codon usage predicted from the observed tRNA
fractions. We also conducted a second simulation with 106
replications, in which tRNA factions vary randomly according to
the above uniform distribution. We then obtained the distribution of
the distance between the observed codon usage and that predicted
from random tRNA fractions. This way, the potential confounding
effect of genomic GC content on the assumed null distribution of
codon usage becomes irrelevant to the test. We similarly tested the
square rule and the truncation rule. Results from the first simulation
are presented in Figure 2D, while those from the second simulation
are in Table S1.
Distance to native codon usage
We developed an index, distance to native codon usage (Dncu),
to measure how different the codon usage of a (heterologous) gene
is from the overall codon usage of the host cell, which is
presumably balanced with tRNA concentrations. First, the
Euclidean distance in synonymous codon usage between the
heterologous gene and the host is calculated for each of the 18
amino acids with at least two synonymous codons by
ð7Þ
j~1
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ð8Þ
where k#18 is the number of amino acid types encoded by the
gene excluding Met and Trp, which have no synonymous codons,
mi is the number of amino acid i found in the protein, and l is the
protein length excluding Met and Trp residues. By definition, Dncu
is between 0 and 1.
Empirical test of the proportional rule
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u ni
uX
Di ~t
(Yij {Xij )2 ,
m 1
Dncu ~( P Di i ) l ,
15
March 2012 | Volume 8 | Issue 3 | e1002603
Codon Usage and Translational Efficiency
relative concentrations of the four tRNA species are 20, 21, 22, and
23, respectively. The digital organism has ten genes with relative
(mRNA and protein) expression levels from 20 to 29, respectively.
These genes each have 12 codons that are sampled from the four
synonymous codons. We started the simulation with exactly the
same usage of the four synonymous codons in each gene.
Synonymous mutations among codons all have the same rates
and the total mutation rate per genome is assumed to be one
synonymous change per generation. The relative CST for a codon
is assumed to equal the number of times the codon is used in
translation divided by the number of corresponding tRNA
molecules. The total time (T) required for translating all the
proteins can be considered as the generation time. T can be
calculated by summing up the CSTs of all codons in all transcripts
if there is only one ribosome in the cell. If there are m ribosomes in
the cell, the time required would simply be m times shorter. Thus,
without loss of generality, we assume m = 1. A strain with a shorter
generation has a higher fitness and will spread in the population.
Genetic drift is simulated by random sampling of cells for the next
generation. The population size is 104 individuals and the
simulation lasts for 500 generations. We repeated the simulation
1000 times. Our results did not change when we simulated the
evolution for more generations. By contrast, when we removed the
natural selection for translational efficiency in simulation, the
phenomena observed in Figure 4 disappeared (Figure S9).
Note that, in the simulation, we allow codon usage to evolve
while fixing tRNA concentrations. If tRNA concentrations evolve
while the codon usage is fixed, we also expect to observe the
rebalance of codon-tRNA usage, but the correlation (or the lack of)
between CUB and gene expression level will not change during
this evolutionary process. In reality, tRNA concentrations and
synonymous codon usage likely co-evolve to regain the balance. As
long as codon usage is allowed to evolve, we expect stronger CUB
to appear in more highly expressed genes, as demonstrated in
Figure 4.
To compare the vYFP mRNA levels among strains, we extracted
the total RNA (RiboPure-Yeast Kit, Ambion) from three
independently transformed strains of each genotype. The total
RNA was reversely transcribed into cDNA (Moloney Murine
Leukemia Virus Reverse Transcriptase, Invitrogen) with random
hexamer primers. The vYFP mRNA level was measured by
quantitative polymerase chain reaction (7300 Real-Time PCR
System, Applied Biosystems) with ACT1 as an internal control.
The primers for vYFP are 59 – CATGGCCAACACTTGTCACT– 39 and 59 –TACATAACCTTCGGGCATGG– 3, while
the primers for ACT1 are 59 - CTGCCGGTATTGACCAAACT 39 and 59 – CGGTGATTTCCTTTTGCATT – 39.
Multivariate regression analysis
The software package RELAIMPO (http://cran.r-project.org/
web/packages/relaimpo/) was used for a multivariate regression
analysis of the yeast experimental data from all cells of the four
strains. We compared the relative importance of Dncu and CAI in
explaining the among-cell variation in vYFP signal by the LMG
method and used 1000 bootstrap replications to determine the
statistical significance. Use of other methods (LAST, FIRST, and
PRATT) implemented in RELAIMPO gave similar results.
Impact of potential errors in translation on our
experiments
Proponents of the translational accuracy hypothesis might argue
that, because different synonymous codons have different
mistranslation rates [52,73] and preferred codons are considered
to be more accurately translated than unpreferred codons [7], the
mCherry with a low CAI is expected to produce fewer functional
protein molecules than the mCherry with a high CAI even when the
same numbers of protein molecules are produced. In other words,
using red florescent signals may have led to a more severe
underestimation of protein expression for the mCherry with a low
CAI than for that with a high CAI. The average mistranslation rate
has been estimated to be ,561024 per codon, and unpreferred
codons have been posited to undergo mistranslation five times as
often as preferred codons [7]. Based on these numbers and the
CAIs of the four mCherry versions (Figure 3B), we assume that the
mistranslation rate is 1061024, 861024, 561024, and 261024
per codon for mCherry-1 to mCherry-4, respectively. Let us further
assume that no mistranslated protein fluoresces. Given the length
of mCherry (236 amino acids), we expect that 11.8%, 9.44%,
5.9%, and 2.36% of mCherry-1 to mCherry-4 proteins respectively fail to fluoresce due to mistranslation. On this assumption,
we corrected mCherry expression levels from the observed
florescent signals. We also conducted a better control of mCherry
expression among strains by dividing cells of each strain into 15
bins based on the above corrected mCherry expression (Figure
S7). Again, we observed a lower vYFP expression when the Dncu of
the mCherry gene is higher, across the range of mCherry
expressions shared by the three strains (Figure S7). This result is
conservative, because only a minority of mistranslations are
expected to prevent fluorescence, and it is likely that we have
overcorrected the effect of mistranslation.
Supporting Information
Figure S1 The procedure for estimating codon selection times
(CSTs) from ribosome profiling data. Circled numbers correspond
to the equations in Materials and Methods and thick arrows show
the iterations.
(PDF)
Figure S2 The estimates of CSTs quickly converge after a few
iterations.
(PDF)
Figure S3 Robustness of CST estimates. (A–B) Comparison of
CST estimates when different initial values of translational
initiation rates are used. (C) CST estimates from two media (rich
and starvation) are similar.
(PDF)
Figure S4 No correlation between codon preference (red dots)
and CST (grey bars) among synonymous codons. CSTs are rescaled
such that the maximal observed value is 1. Error bars show one
standard error, estimated by the bootstrap method. Following ref.
1 in the main text, we measured the preference of a codon by
Spearman’s rank correlation (r) between the frequency of the
codon in a gene and the effective number of codons in the gene
(Ncp) across all genes (see Supplementary Methods). Preferred
codons have more negative r values.
(PDF)
Computer simulation of the evolution of synonymous
codon usage bias
We simulated the evolution of synonymous codon usage in an
asexual haploid unicellular digital organism. In this organism, we
focused on a single amino acid with four synonymous codons
(codon1 to codon4) that are respectively recognized by four
distinct tRNA species (tRNA1 to tRNA4). We assume that the
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Figure S5 Alignment of the DNA sequences of the four
synonymous versions of mCherry used in our yeast experiments.
16
March 2012 | Volume 8 | Issue 3 | e1002603
Codon Usage and Translational Efficiency
Figure S11 Correlation between the total tRNA gene copy
number for an amino acid and the mRNA expression level of the
corresponding aminoacyl tRNA synthetase. Each dot represents
an amino acid. Only 18 amino acids are presented because of the
lack of information for the synthetases of Pro and Glu. The
aminoacyl tRNA synthetase genes were identified based on gene
annotations in SGD (http://www.yeastgenome.org/) and the
expression levels of these genes were obtained from Holstege et
al. (1998 Cell 95, 717).
(PDF)
Invariant sites among the four sequences are marked with
asterisks.
(PDF)
Figure S6 Codon usage of four synonymous versions of mCherry
and that of the native transcriptome, compared to relative
concentrations of cognate tRNAs in S. cerevisiae, for the 12 amino
acids that have at least two isoaccepting tRNA species.
(PDF)
Figure S7 The impact of synonymous codon usage of mCherry on
vYFP expression is not explainable by the translational accuracy
hypothesis. The mCherry expression levels have been corrected by
considering mistranslations that reduce the red florescent signals of
mCherry. Mistranslation rates are assumed to be 1061024,
861024, 561024 and 261024 per codon for mCherry-1 to mCherry4, respectively. Our results are not sensitive to these assumptions of
mistranslation rates. Cells of each strain are then divided into 15
equal-size bins by the corrected mCherry expression level per unit
cell size. Error bars show one standard error.
(PDF)
Figure S12 Significantly different CSTs among different amino
acids. To quantify potential variations in CST among amino acids
and among synonymous codons, we linearly regressed the CSTs of
the 61 sense codons using the formula of CSTij ~ai zbtij zcze,
where CSTij is the CST of the jth codon of the ith amino acid, ai is
the effect of amino acid i, b is the coefficient for the tRNA effect, tij
is the gene copy number for the cognate tRNA of the jth codon of
the ith amino acid, c is a constant equal to the mean CST of all
sense codons, and e is the residual effect. The parameters in the
above linear regression were estimated by the least squares
method. Asterisks indicate a statistically significant effect
(*, nominal P,5%; **, nominal P,1%). Note the lack of a
significant effect of the cognate tRNA gene copy number on the
variation of synonymous CSTs, consistent with the results in
Figure 1.
(PDF)
Figure S8 Distribution of the Euclidian distance in codon usage
between all yeast genes and (A) mCherry-1, (B) mCherry-2, (C)
mCherry-3, and (D) mCherry-4. Euclidian distance is calculated by
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u 61
uX
t
(xi {yi )2 , where xi is the frequency of codon i in mCherry and
i~1
yi is the corresponding frequency in a yeast gene. The distance
between vYFP and mCherry is indicated by the arrow.
(PDF)
Euclidian and Manhattan distances between the
observed tRNA fractions and the predictions of the proportional
rule, square rule, and truncation rule, respectively.
(PDF)
Table S1
Figure S9 Results from computer simulations without selection
for translational efficiency. The simulations are conducted as
described in Materials and Methods, except that no selection for
translational efficiency is applied. (A) Overall changes of
transcriptomic codon usage averaged from 1000 simulation
replications. Error bars show one standard deviation. (B) No
significant difference in codon usage among genes of different
expression levels. The averages from 1000 simulation replications
are presented. Error bars show one standard deviation.
(PDF)
Table S2 Unequal use of iso-synonymous codons in S. cerevisiae.
(PDF)
Acknowledgments
We thank Barry Williams for discussion and Hiroshi Akashi, Meg
Bakewell, Chungoo Park, Josh Plotkin, and Claus Wilke for valuable
comments.
Author Contributions
Figure S10 High correlation between amino acid frequencies
inferred from yeast transcriptomic data and those from yeast
proteomic data. Each dot represents an amino acid.
(PDF)
Conceived and designed the experiments: WQ JZ. Performed the
experiments: WQ NMP CM. Analyzed the data: WQ J-RY. Contributed
reagents/materials/analysis tools: JZ. Wrote the paper: WQ JZ.
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March 2012 | Volume 8 | Issue 3 | e1002603